Information complexity-based regularization parameter selection for solution of ill conditioned inverse problems

DOI10.1088/0266-5611/18/2/101zbMath0995.65052OpenAlexW2171548488MaRDI QIDQ4531333

Hamparsum Bozdogan, Robert E. Uhrig, Andrei V. Gribok, J. Wesley Hines, Aleksey M. Urmanov

Publication date: 23 October 2002

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/b37fb1ef0b38f95a48c87bf044e37ed61047a522

zbMATH Keywords

performancetest problemparameter selectionnon-Gaussian noiseKullback-Leibler distanceill-conditioned inverse problemsinformation complexity-based regularization

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) Iterative numerical methods for linear systems (65F10) Complexity and performance of numerical algorithms (65Y20)

Related Items

Exploiting residual information in the parameter choice for discrete ill-posed problems ⋮ Data reduction for multiple functional data with class information ⋮ Regularized total least squares based on quadratic eigenvalue problem solvers

Uses Software

• Regularization tools